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CompPhysics / repository
This repository contains exercises and projects on computational science and AI for the CompSci program. Lecture notes at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html
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This repository contains exercises and projects on computational science and AI for the CompSci program
Machine learning with linear and non-linear regression, logistic regression and support vector machines as well as Bayesian linear regression. This involves linear algebra (matrix inversion, determinants, eigenvalues, SVD and more from FYS4150), convex optimization problem (gradient descent, steepest descent, stochastic gradient descent, iterative solvers) and several central (deterministic) ML methods. Calculation-oriented statistics with Bayes' theorem and MCMC sampling can also be included. Bayesian linear regression can be omitted.
Datasets you study can be adapted to your research field, whether it is astro, physics, chemistry, bioscience, geoscience or mathematics. Planned finished end January 2023
Deep learning: standard neural networks, convolution and neural networks (CNN), recursive neural networks, Boltzmann machines, various autoencoders and possibly general adversial networks. Reduction of dimensionality in scientific problems. Possible topic to work with: solution of ordinary and partial differential equations. Here we can take this from a deep learning perspective and a traditional final difference form taught in FYS4150. But we can also focus on classification problems. Datasets can again be adapted to the field.
See project description here: https://raw.githubusercontent.com/CompPhysics/CompSciProgram/main/doc/Projects/2022/Project3/pdf/Project3.pdf
In total 20 ECTS.
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